ALM is an adaptive recursive algorithm which tries to express a multi-input multi-output system as a fuzzy combination of some single-input single-output systems. It uses a fuzzy curve fitting technique for behavior extraction or finding the input-output transformation of each of the single-input single-output systems, which called ink drop spread (IDS). In this paper we present a new implementation of a hardware unit implementing the ink drop spread (IDS) method.

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Analog dividers are widely used in analog systems. Analog realization of such circuits suffer from limited dynamic range and non-linearity issues, therefore, extra circuitry should be required to compensate these types of shortcomings. In this paper a gain controllable, analog divider is proposed based on data converters. Our circuit can be implemented both in current and voltage mode by selecting proper architectures. The resolution, power consumption and operation speed can be controlled by proper selecting of components. Another advantage of our circuit is its gain programmability. Moreover, the gain can be adjusted independently based on the relationship between input signals. Our proposed method offers two different gain control abilities, one for situation that the numerator signal is bigger than the denominator, and another gain is applied when the denominator is larger than the numerator. As a result, no extra amplifier is required for signal amplification. Moreover, the input and output signal nature can be chosen arbitrarily in this circuit, i.e. input signal may be a voltage signal while the output signal is current. Simulation results from SPICE confirm the proper operation of the circuit.

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Recent years have seen fast advances in neural recording circuits and systems as they offer a promising way to investigate real-time brain monitoring and the closed-loop modulation of psychological disorders and neurodegenerative diseases. In this context, this tutorial brief presents a concise overview of concepts and design methodologies of neural recording, highlighting neural signal characteristics, system-level specifications and architectures, circuit-level implementation, and noise reduction techniques. Future trends and challenges of neural recording are finally discussed.

The input-referred noise (IRN) is one of the most crucial performance indicators for analog front-ends (AFEs) of neural recording devices. We present in this paper, a novel design approach for a low-noise amplifier (LNA) based on the transistor optimization method in CMOS technology. Because flicker noise is predominant in neural recording applications, the AFE has been designed so as to meet input-referred flicker noise specifications, whereas thermal noise contributions are monitored and controlled by flicker noise corner frequencies. Transistor optimization is accomplished by using a lookup table that encapsulates its performance based on its current density. Initially, transistors are optimized based on flicker noise performance; later, they may be further optimized based on their size, power consumption, transconductance, or thermal noise contribution. The proposed approach has been validated by designing a folded-cascode amplifier with an IRN ranging from 2 to 8 µV rms . The results of the simulation show that the errors of our design methodology are less than 10%, which is less than those of gm /I D and the inversion coefficient methods. The proposed LNA achieves 2.1 µV rms while consuming 0.83 µW from 1.2 V supply.